Case data from 200 morphine-involved deaths (Spiehler, V. and Brown, R., Journal of Forensic Sciences, Vol. 32, No. 4, July 1987, pp. 906–916) were analyzed for patterns and relationships using artificial intelligence (AI) computer software. Case parameters were blood unconjugated morphine, blood, brain, and liver total morphine, sex, age, frequency of use, time of death after injection, cause of death, and presence of other drugs. The programs used were Expert 4 (Biosoft-Cambridge), BEAGLE (Warm Boot Ltd.), and KnowledgeMaker (Knowledge Garden Inc.). Interpretation was defined as estimating the dose, response, and time after drug dosing.

The AI programs were used to advise on time and response outcomes for cases, to calculate the probability of the estimate being true, to develop rules for interpretation of morphine-involved cases, and to diagram a decision tree. On known cases the AI programs were successful 70 to 90% of the time in classifying the cases as to response and time. No data on dose were available in this database. The success rate in individual cases was proportional to the program-estimated probability. All three programs found the case parameters of most value in predicting response to be blood unconjugated morphine, blood total morphine, and liver total morphine. The case data most useful in estimating time of death since drug injection were blood unconjugated morphine, percent unconjugated morphine in blood, and brain total morphine. The rule induction programs found that morphine overdoses were characterized by blood unconjugated morphine greater than 0.24 µg/mL, liver morphine greater than 0.50 to 0.75 µg/g, brain morphine greater than 0.08 µg/g or greater than blood unconjugated morphine, and percent blood unconjugated morphine greater than 37%. Rapid deaths were characterized by percent unconjugated morphine greater than 44 to 50%; blood unconjugated morphine, as a function of other drugs present, greater than 0.09 to 0.21 µg/mL; and brain total morphine greater than 0.16 to 0.22 µg/g.

This work demonstrates that inexpensive AI programs commercially available for personal computers can be useful in interpretation in forensic toxicology.